Overview

Dataset statistics

Number of variables20
Number of observations1320
Missing cells198
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory206.4 KiB
Average record size in memory160.1 B

Variable types

Text2
Categorical6
Numeric10
DateTime2

Alerts

Energy Consumed (kWh) has 66 (5.0%) missing valuesMissing
Charging Rate (kW) has 66 (5.0%) missing valuesMissing
Distance Driven (since last charge) (km) has 66 (5.0%) missing valuesMissing
User ID has unique valuesUnique
Charging Start Time has unique valuesUnique
Charging Duration (hours) has unique valuesUnique
Charging Cost (USD) has unique valuesUnique
State of Charge (Start %) has unique valuesUnique
State of Charge (End %) has unique valuesUnique
Temperature (°C) has unique valuesUnique
Vehicle Age (years) has 143 (10.8%) zerosZeros

Reproduction

Analysis started2024-10-07 11:38:28.307568
Analysis finished2024-10-07 11:38:41.717853
Duration13.41 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

User ID
Text

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:42.079201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1613636
Min length6

Characters and Unicode

Total characters10773
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1320 ?
Unique (%)100.0%

Sample

1st rowUser_1
2nd rowUser_2
3rd rowUser_3
4th rowUser_4
5th rowUser_5
ValueCountFrequency (%)
user_1 1
 
0.1%
user_3 1
 
0.1%
user_5 1
 
0.1%
user_6 1
 
0.1%
user_7 1
 
0.1%
user_8 1
 
0.1%
user_9 1
 
0.1%
user_10 1
 
0.1%
user_11 1
 
0.1%
user_12 1
 
0.1%
Other values (1310) 1310
99.2%
2024-10-07T07:38:42.641101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1320
12.3%
s 1320
12.3%
e 1320
12.3%
r 1320
12.3%
_ 1320
12.3%
1 793
7.4%
2 463
 
4.3%
3 383
 
3.6%
8 362
 
3.4%
7 362
 
3.4%
Other values (5) 1810
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1320
12.3%
s 1320
12.3%
e 1320
12.3%
r 1320
12.3%
_ 1320
12.3%
1 793
7.4%
2 463
 
4.3%
3 383
 
3.6%
8 362
 
3.4%
7 362
 
3.4%
Other values (5) 1810
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1320
12.3%
s 1320
12.3%
e 1320
12.3%
r 1320
12.3%
_ 1320
12.3%
1 793
7.4%
2 463
 
4.3%
3 383
 
3.6%
8 362
 
3.4%
7 362
 
3.4%
Other values (5) 1810
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1320
12.3%
s 1320
12.3%
e 1320
12.3%
r 1320
12.3%
_ 1320
12.3%
1 793
7.4%
2 463
 
4.3%
3 383
 
3.6%
8 362
 
3.4%
7 362
 
3.4%
Other values (5) 1810
16.8%

Vehicle Model
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Tesla Model 3
280 
Hyundai Kona
266 
Nissan Leaf
260 
BMW i3
258 
Chevy Bolt
256 

Length

Max length13
Median length11
Mean length10.454545
Min length6

Characters and Unicode

Total characters13800
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBMW i3
2nd rowHyundai Kona
3rd rowChevy Bolt
4th rowHyundai Kona
5th rowHyundai Kona

Common Values

ValueCountFrequency (%)
Tesla Model 3 280
21.2%
Hyundai Kona 266
20.2%
Nissan Leaf 260
19.7%
BMW i3 258
19.5%
Chevy Bolt 256
19.4%

Length

2024-10-07T07:38:42.807922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T07:38:42.959078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
tesla 280
9.6%
model 280
9.6%
3 280
9.6%
hyundai 266
9.1%
kona 266
9.1%
nissan 260
8.9%
leaf 260
8.9%
bmw 258
8.8%
i3 258
8.8%
chevy 256
8.8%

Most occurring characters

ValueCountFrequency (%)
1600
 
11.6%
a 1332
 
9.7%
e 1076
 
7.8%
l 816
 
5.9%
o 802
 
5.8%
s 800
 
5.8%
n 792
 
5.7%
i 784
 
5.7%
d 546
 
4.0%
M 538
 
3.9%
Other values (15) 4714
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1600
 
11.6%
a 1332
 
9.7%
e 1076
 
7.8%
l 816
 
5.9%
o 802
 
5.8%
s 800
 
5.8%
n 792
 
5.7%
i 784
 
5.7%
d 546
 
4.0%
M 538
 
3.9%
Other values (15) 4714
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1600
 
11.6%
a 1332
 
9.7%
e 1076
 
7.8%
l 816
 
5.9%
o 802
 
5.8%
s 800
 
5.8%
n 792
 
5.7%
i 784
 
5.7%
d 546
 
4.0%
M 538
 
3.9%
Other values (15) 4714
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1600
 
11.6%
a 1332
 
9.7%
e 1076
 
7.8%
l 816
 
5.9%
o 802
 
5.8%
s 800
 
5.8%
n 792
 
5.7%
i 784
 
5.7%
d 546
 
4.0%
M 538
 
3.9%
Other values (15) 4714
34.2%

Battery Capacity (kWh)
Real number (ℝ)

Distinct147
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.534692
Minimum1.5328065
Maximum193.00307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:43.124418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5328065
5-th percentile50
Q162
median75
Q385
95-th percentile100
Maximum193.00307
Range191.47027
Interquartile range (IQR)23

Descriptive statistics

Standard deviation20.626914
Coefficient of variation (CV)0.27674246
Kurtosis2.4233082
Mean74.534692
Median Absolute Deviation (MAD)13
Skewness0.37344004
Sum98385.793
Variance425.46959
MonotonicityNot monotonic
2024-10-07T07:38:43.332611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 248
18.8%
62 238
18.0%
85 235
17.8%
100 233
17.7%
50 224
17.0%
108.4630074 1
 
0.1%
43.71913702 1
 
0.1%
140.7620616 1
 
0.1%
96.60872071 1
 
0.1%
73.85002716 1
 
0.1%
Other values (137) 137
10.4%
ValueCountFrequency (%)
1.53280653 1
0.1%
1.536539743 1
0.1%
3.838518074 1
0.1%
3.976596499 1
0.1%
5.189529952 1
0.1%
6.168895844 1
0.1%
7.451954887 1
0.1%
7.714516854 1
0.1%
10.18928677 1
0.1%
12.03333697 1
0.1%
ValueCountFrequency (%)
193.0030739 1
0.1%
188.6349646 1
0.1%
179.0130578 1
0.1%
174.4096677 1
0.1%
157.5757789 1
0.1%
147.3953543 1
0.1%
143.4752097 1
0.1%
143.2099946 1
0.1%
140.7620616 1
0.1%
140.5136826 1
0.1%
Distinct462
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:43.685775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.767424
Min length9

Characters and Unicode

Total characters14213
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)6.9%

Sample

1st rowStation_391
2nd rowStation_428
3rd rowStation_181
4th rowStation_327
5th rowStation_108
ValueCountFrequency (%)
station_108 9
 
0.7%
station_97 7
 
0.5%
station_74 7
 
0.5%
station_10 7
 
0.5%
station_44 7
 
0.5%
station_461 7
 
0.5%
station_17 7
 
0.5%
station_181 6
 
0.5%
station_336 6
 
0.5%
station_19 6
 
0.5%
Other values (452) 1251
94.8%
2024-10-07T07:38:44.299448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2640
18.6%
S 1320
9.3%
a 1320
9.3%
i 1320
9.3%
o 1320
9.3%
n 1320
9.3%
_ 1320
9.3%
1 546
 
3.8%
4 533
 
3.8%
2 524
 
3.7%
Other values (7) 2050
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2640
18.6%
S 1320
9.3%
a 1320
9.3%
i 1320
9.3%
o 1320
9.3%
n 1320
9.3%
_ 1320
9.3%
1 546
 
3.8%
4 533
 
3.8%
2 524
 
3.7%
Other values (7) 2050
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2640
18.6%
S 1320
9.3%
a 1320
9.3%
i 1320
9.3%
o 1320
9.3%
n 1320
9.3%
_ 1320
9.3%
1 546
 
3.8%
4 533
 
3.8%
2 524
 
3.7%
Other values (7) 2050
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2640
18.6%
S 1320
9.3%
a 1320
9.3%
i 1320
9.3%
o 1320
9.3%
n 1320
9.3%
_ 1320
9.3%
1 546
 
3.8%
4 533
 
3.8%
2 524
 
3.7%
Other values (7) 2050
14.4%
Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Los Angeles
297 
San Francisco
264 
Houston
262 
New York
255 
Chicago
242 

Length

Max length13
Median length11
Mean length9.2931818
Min length7

Characters and Unicode

Total characters12267
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouston
2nd rowSan Francisco
3rd rowSan Francisco
4th rowHouston
5th rowLos Angeles

Common Values

ValueCountFrequency (%)
Los Angeles 297
22.5%
San Francisco 264
20.0%
Houston 262
19.8%
New York 255
19.3%
Chicago 242
18.3%

Length

2024-10-07T07:38:44.485584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T07:38:44.628419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
los 297
13.9%
angeles 297
13.9%
san 264
12.4%
francisco 264
12.4%
houston 262
12.3%
new 255
11.9%
york 255
11.9%
chicago 242
11.3%

Most occurring characters

ValueCountFrequency (%)
o 1582
12.9%
s 1120
 
9.1%
n 1087
 
8.9%
e 849
 
6.9%
816
 
6.7%
a 770
 
6.3%
c 770
 
6.3%
g 539
 
4.4%
r 519
 
4.2%
i 506
 
4.1%
Other values (14) 3709
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1582
12.9%
s 1120
 
9.1%
n 1087
 
8.9%
e 849
 
6.9%
816
 
6.7%
a 770
 
6.3%
c 770
 
6.3%
g 539
 
4.4%
r 519
 
4.2%
i 506
 
4.1%
Other values (14) 3709
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1582
12.9%
s 1120
 
9.1%
n 1087
 
8.9%
e 849
 
6.9%
816
 
6.7%
a 770
 
6.3%
c 770
 
6.3%
g 539
 
4.4%
r 519
 
4.2%
i 506
 
4.1%
Other values (14) 3709
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1582
12.9%
s 1120
 
9.1%
n 1087
 
8.9%
e 849
 
6.9%
816
 
6.7%
a 770
 
6.3%
c 770
 
6.3%
g 539
 
4.4%
r 519
 
4.2%
i 506
 
4.1%
Other values (14) 3709
30.2%

Charging Start Time
Date

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Minimum2024-01-01 00:00:00
Maximum2024-02-24 23:00:00
2024-10-07T07:38:44.981129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:45.169477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1309
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Minimum2024-01-01 00:39:00
Maximum2024-02-24 23:56:00
2024-10-07T07:38:45.376417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:45.656741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Energy Consumed (kWh)
Real number (ℝ)

MISSING 

Distinct1254
Distinct (%)100.0%
Missing66
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean42.642894
Minimum0.045771842
Maximum152.23876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:45.976058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.045771842
5-th percentile7.7893356
Q123.881193
median42.691405
Q361.206218
95-th percentile76.621048
Maximum152.23876
Range152.19299
Interquartile range (IQR)37.325025

Descriptive statistics

Standard deviation22.411705
Coefficient of variation (CV)0.52556715
Kurtosis-0.59023791
Mean42.642894
Median Absolute Deviation (MAD)18.773442
Skewness0.10208821
Sum53474.189
Variance502.2845
MonotonicityNot monotonic
2024-10-07T07:38:46.255023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.71234573 1
 
0.1%
9.047732107 1
 
0.1%
78.16770975 1
 
0.1%
20.17078858 1
 
0.1%
57.67538256 1
 
0.1%
68.21934426 1
 
0.1%
77.45420996 1
 
0.1%
36.26259614 1
 
0.1%
51.08539267 1
 
0.1%
28.78049141 1
 
0.1%
Other values (1244) 1244
94.2%
(Missing) 66
 
5.0%
ValueCountFrequency (%)
0.04577184228 1
0.1%
0.121143295 1
0.1%
1.330225739 1
0.1%
1.356731547 1
0.1%
1.754520234 1
0.1%
4.288320356 1
0.1%
5.013889647 1
0.1%
5.144893572 1
0.1%
5.163807088 1
0.1%
5.200112601 1
0.1%
ValueCountFrequency (%)
152.238758 1
0.1%
127.7574744 1
0.1%
103.8126056 1
0.1%
97.66776539 1
0.1%
94.36532941 1
0.1%
90.23550358 1
0.1%
85.03985431 1
0.1%
80.72941928 1
0.1%
79.97807284 1
0.1%
79.97267191 1
0.1%

Charging Duration (hours)
Real number (ℝ)

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2693774
Minimum0.095314417
Maximum7.6351448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:46.522820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.095314417
5-th percentile0.67490758
Q11.3976234
median2.2581361
Q33.1128064
95-th percentile3.8279991
Maximum7.6351448
Range7.5398303
Interquartile range (IQR)1.7151829

Descriptive statistics

Standard deviation1.0610368
Coefficient of variation (CV)0.46754534
Kurtosis0.23963333
Mean2.2693774
Median Absolute Deviation (MAD)0.85908509
Skewness0.36130865
Sum2995.5782
Variance1.1257991
MonotonicityNot monotonic
2024-10-07T07:38:46.821919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5913634254 1
 
0.1%
3.443237131 1
 
0.1%
2.450451566 1
 
0.1%
2.879571205 1
 
0.1%
0.8864503814 1
 
0.1%
3.308261928 1
 
0.1%
2.012360141 1
 
0.1%
0.9497561026 1
 
0.1%
1.967714479 1
 
0.1%
0.7713119843 1
 
0.1%
Other values (1310) 1310
99.2%
ValueCountFrequency (%)
0.09531441655 1
0.1%
0.1400299896 1
0.1%
0.1543006839 1
0.1%
0.1653938787 1
0.1%
0.182479567 1
0.1%
0.2066308831 1
0.1%
0.3599172409 1
0.1%
0.4218407427 1
0.1%
0.4348899348 1
0.1%
0.4764888585 1
0.1%
ValueCountFrequency (%)
7.635144759 1
0.1%
6.773095327 1
0.1%
6.75915168 1
0.1%
6.494007043 1
0.1%
6.17641656 1
0.1%
5.945571288 1
0.1%
5.673788713 1
0.1%
5.505495842 1
0.1%
5.444091596 1
0.1%
5.220359375 1
0.1%

Charging Rate (kW)
Real number (ℝ)

MISSING 

Distinct1254
Distinct (%)100.0%
Missing66
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean25.963003
Minimum1.4725491
Maximum97.342255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:47.078804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.4725491
5-th percentile5.3107282
Q113.856583
median25.603799
Q337.502998
95-th percentile47.750658
Maximum97.342255
Range95.869706
Interquartile range (IQR)23.646415

Descriptive statistics

Standard deviation14.011326
Coefficient of variation (CV)0.53966508
Kurtosis-0.46614924
Mean25.963003
Median Absolute Deviation (MAD)11.841471
Skewness0.24857628
Sum32557.606
Variance196.31727
MonotonicityNot monotonic
2024-10-07T07:38:47.290855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.43474941 1
 
0.1%
40.42078196 1
 
0.1%
46.31316025 1
 
0.1%
14.88435406 1
 
0.1%
29.41340669 1
 
0.1%
38.149082 1
 
0.1%
25.83848785 1
 
0.1%
40.91079821 1
 
0.1%
7.446768634 1
 
0.1%
36.38918057 1
 
0.1%
Other values (1244) 1244
94.2%
(Missing) 66
 
5.0%
ValueCountFrequency (%)
1.472549139 1
0.1%
1.654937068 1
0.1%
1.722296154 1
0.1%
2.246134508 1
0.1%
2.535237862 1
0.1%
2.560943434 1
0.1%
2.63696679 1
0.1%
2.826146252 1
0.1%
3.023485282 1
0.1%
3.508161487 1
0.1%
ValueCountFrequency (%)
97.3422547 1
0.1%
74.61279366 1
0.1%
71.62104096 1
0.1%
68.54260187 1
0.1%
67.93467893 1
0.1%
63.21611838 1
0.1%
59.14854489 1
0.1%
57.09975217 1
0.1%
53.1747828 1
0.1%
51.36359325 1
0.1%

Charging Cost (USD)
Real number (ℝ)

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.551352
Minimum0.23431699
Maximum69.407743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:47.492772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.23431699
5-th percentile7.052738
Q113.368141
median22.07636
Q331.646044
95-th percentile38.303231
Maximum69.407743
Range69.173426
Interquartile range (IQR)18.277903

Descriptive statistics

Standard deviation10.751494
Coefficient of variation (CV)0.47675606
Kurtosis-0.48734653
Mean22.551352
Median Absolute Deviation (MAD)9.1103733
Skewness0.25091905
Sum29767.784
Variance115.59462
MonotonicityNot monotonic
2024-10-07T07:38:47.688271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.08771679 1
 
0.1%
35.72732643 1
 
0.1%
37.25540969 1
 
0.1%
33.67417017 1
 
0.1%
17.26707379 1
 
0.1%
5.691471522 1
 
0.1%
35.14611587 1
 
0.1%
7.181392583 1
 
0.1%
25.26991752 1
 
0.1%
27.52125904 1
 
0.1%
Other values (1310) 1310
99.2%
ValueCountFrequency (%)
0.2343169936 1
0.1%
0.3070854525 1
0.1%
0.6713465052 1
0.1%
1.640802843 1
0.1%
2.348075123 1
0.1%
3.059041507 1
0.1%
3.668584746 1
0.1%
3.707066616 1
0.1%
5.043208361 1
0.1%
5.056096912 1
0.1%
ValueCountFrequency (%)
69.40774319 1
0.1%
68.93168829 1
0.1%
60.40252537 1
0.1%
58.74667669 1
0.1%
58.25460657 1
0.1%
54.89298253 1
0.1%
49.13746988 1
0.1%
46.27027201 1
0.1%
46.17291744 1
0.1%
46.05917303 1
0.1%

Time of Day
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Evening
362 
Morning
336 
Night
312 
Afternoon
310 

Length

Max length9
Median length7
Mean length6.9969697
Min length5

Characters and Unicode

Total characters9236
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowMorning
3rd rowMorning
4th rowEvening
5th rowMorning

Common Values

ValueCountFrequency (%)
Evening 362
27.4%
Morning 336
25.5%
Night 312
23.6%
Afternoon 310
23.5%

Length

2024-10-07T07:38:47.917043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T07:38:48.052666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
evening 362
27.4%
morning 336
25.5%
night 312
23.6%
afternoon 310
23.5%

Most occurring characters

ValueCountFrequency (%)
n 2016
21.8%
i 1010
10.9%
g 1010
10.9%
o 956
10.4%
e 672
 
7.3%
r 646
 
7.0%
t 622
 
6.7%
E 362
 
3.9%
v 362
 
3.9%
M 336
 
3.6%
Other values (4) 1244
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2016
21.8%
i 1010
10.9%
g 1010
10.9%
o 956
10.4%
e 672
 
7.3%
r 646
 
7.0%
t 622
 
6.7%
E 362
 
3.9%
v 362
 
3.9%
M 336
 
3.6%
Other values (4) 1244
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2016
21.8%
i 1010
10.9%
g 1010
10.9%
o 956
10.4%
e 672
 
7.3%
r 646
 
7.0%
t 622
 
6.7%
E 362
 
3.9%
v 362
 
3.9%
M 336
 
3.6%
Other values (4) 1244
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2016
21.8%
i 1010
10.9%
g 1010
10.9%
o 956
10.4%
e 672
 
7.3%
r 646
 
7.0%
t 622
 
6.7%
E 362
 
3.9%
v 362
 
3.9%
M 336
 
3.6%
Other values (4) 1244
13.5%

Day of Week
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Saturday
205 
Tuesday
200 
Wednesday
197 
Sunday
191 
Friday
188 
Other values (2)
339 

Length

Max length9
Median length8
Mean length7.1431818
Min length6

Characters and Unicode

Total characters9429
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowMonday
3rd rowThursday
4th rowSaturday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Saturday 205
15.5%
Tuesday 200
15.2%
Wednesday 197
14.9%
Sunday 191
14.5%
Friday 188
14.2%
Monday 185
14.0%
Thursday 154
11.7%

Length

2024-10-07T07:38:48.188339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T07:38:48.337504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
saturday 205
15.5%
tuesday 200
15.2%
wednesday 197
14.9%
sunday 191
14.5%
friday 188
14.2%
monday 185
14.0%
thursday 154
11.7%

Most occurring characters

ValueCountFrequency (%)
a 1525
16.2%
d 1517
16.1%
y 1320
14.0%
u 750
8.0%
e 594
 
6.3%
n 573
 
6.1%
s 551
 
5.8%
r 547
 
5.8%
S 396
 
4.2%
T 354
 
3.8%
Other values (7) 1302
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1525
16.2%
d 1517
16.1%
y 1320
14.0%
u 750
8.0%
e 594
 
6.3%
n 573
 
6.1%
s 551
 
5.8%
r 547
 
5.8%
S 396
 
4.2%
T 354
 
3.8%
Other values (7) 1302
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1525
16.2%
d 1517
16.1%
y 1320
14.0%
u 750
8.0%
e 594
 
6.3%
n 573
 
6.1%
s 551
 
5.8%
r 547
 
5.8%
S 396
 
4.2%
T 354
 
3.8%
Other values (7) 1302
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1525
16.2%
d 1517
16.1%
y 1320
14.0%
u 750
8.0%
e 594
 
6.3%
n 573
 
6.1%
s 551
 
5.8%
r 547
 
5.8%
S 396
 
4.2%
T 354
 
3.8%
Other values (7) 1302
13.8%

State of Charge (Start %)
Real number (ℝ)

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.130012
Minimum2.325959
Maximum152.48976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:48.538171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.325959
5-th percentile13.90874
Q127.786903
median48.241771
Q369.277921
95-th percentile86.849574
Maximum152.48976
Range150.1638
Interquartile range (IQR)41.491018

Descriptive statistics

Standard deviation24.074134
Coefficient of variation (CV)0.49000872
Kurtosis-0.78437515
Mean49.130012
Median Absolute Deviation (MAD)20.589119
Skewness0.20523917
Sum64851.616
Variance579.56393
MonotonicityNot monotonic
2024-10-07T07:38:48.695125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.37157598 1
 
0.1%
66.79198382 1
 
0.1%
31.5569263 1
 
0.1%
67.4595416 1
 
0.1%
38.03890271 1
 
0.1%
80.65255109 1
 
0.1%
58.31902031 1
 
0.1%
44.70187213 1
 
0.1%
86.31805713 1
 
0.1%
27.2394756 1
 
0.1%
Other values (1310) 1310
99.2%
ValueCountFrequency (%)
2.325958995 1
0.1%
2.481152833 1
0.1%
5.065719156 1
0.1%
6.854604444 1
0.1%
7.174271255 1
0.1%
7.520444727 1
0.1%
10.00881854 1
0.1%
10.11577764 1
0.1%
10.28531461 1
0.1%
10.42770632 1
0.1%
ValueCountFrequency (%)
152.4897609 1
0.1%
125.0872271 1
0.1%
121.8472807 1
0.1%
119.0511075 1
0.1%
117.656235 1
0.1%
112.4907761 1
0.1%
109.2657943 1
0.1%
105.032224 1
0.1%
102.5758268 1
0.1%
98.33279531 1
0.1%

State of Charge (End %)
Real number (ℝ)

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.14159
Minimum7.6042245
Maximum177.70867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:48.852958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.6042245
5-th percentile51.818356
Q162.053266
median75.682496
Q388.20137
95-th percentile98.23805
Maximum177.70867
Range170.10444
Interquartile range (IQR)26.148104

Descriptive statistics

Standard deviation17.08058
Coefficient of variation (CV)0.22731193
Kurtosis2.3439056
Mean75.14159
Median Absolute Deviation (MAD)13.152206
Skewness0.28203982
Sum99186.899
Variance291.7462
MonotonicityNot monotonic
2024-10-07T07:38:49.042519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.11996244 1
 
0.1%
76.50410671 1
 
0.1%
97.82793195 1
 
0.1%
64.97948546 1
 
0.1%
62.46309786 1
 
0.1%
90.08739344 1
 
0.1%
67.00704756 1
 
0.1%
82.6285391 1
 
0.1%
82.64673079 1
 
0.1%
94.99744371 1
 
0.1%
Other values (1310) 1310
99.2%
ValueCountFrequency (%)
7.604224498 1
0.1%
10.08007433 1
0.1%
14.98994632 1
0.1%
15.71797517 1
0.1%
18.70034949 1
0.1%
18.83987639 1
0.1%
19.57180031 1
0.1%
21.88022094 1
0.1%
22.27521585 1
0.1%
22.88625804 1
0.1%
ValueCountFrequency (%)
177.7086665 1
0.1%
159.9889032 1
0.1%
150.7881071 1
0.1%
147.4921301 1
0.1%
146.8476439 1
0.1%
146.7594514 1
0.1%
140.3830477 1
0.1%
139.8974081 1
0.1%
133.6294352 1
0.1%
132.9520105 1
0.1%

Distance Driven (since last charge) (km)
Real number (ℝ)

MISSING 

Distinct1254
Distinct (%)100.0%
Missing66
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean153.59679
Minimum0.86236133
Maximum398.36477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:49.188372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.86236133
5-th percentile23.567687
Q179.445335
median152.25987
Q3226.07328
95-th percentile286.39396
Maximum398.36477
Range397.50241
Interquartile range (IQR)146.62795

Descriptive statistics

Standard deviation86.004987
Coefficient of variation (CV)0.55994001
Kurtosis-1.1217648
Mean153.59679
Median Absolute Deviation (MAD)73.209761
Skewness0.090422552
Sum192610.37
Variance7396.8577
MonotonicityNot monotonic
2024-10-07T07:38:49.335163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
293.6021106 1
 
0.1%
139.9387004 1
 
0.1%
221.378324 1
 
0.1%
238.8650098 1
 
0.1%
85.38239414 1
 
0.1%
121.4352927 1
 
0.1%
286.8427866 1
 
0.1%
162.4659355 1
 
0.1%
244.8637199 1
 
0.1%
114.1895795 1
 
0.1%
Other values (1244) 1244
94.2%
(Missing) 66
 
5.0%
ValueCountFrequency (%)
0.8623613316 1
0.1%
1.899537885 1
0.1%
2.908369295 1
0.1%
3.824527427 1
0.1%
7.619894391 1
0.1%
10.02829517 1
0.1%
10.44192923 1
0.1%
10.87998409 1
0.1%
10.89843061 1
0.1%
10.93101298 1
0.1%
ValueCountFrequency (%)
398.3647747 1
0.1%
397.8180926 1
0.1%
384.6905686 1
0.1%
369.8535236 1
0.1%
327.3930934 1
0.1%
324.5247972 1
0.1%
311.6304081 1
0.1%
302.8524897 1
0.1%
299.7598782 1
0.1%
299.6629508 1
0.1%

Temperature (°C)
Real number (ℝ)

UNIQUE 

Distinct1320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.263591
Minimum-10.72477
Maximum73.169588
Zeros0
Zeros (%)0.0%
Negative257
Negative (%)19.5%
Memory size10.4 KiB
2024-10-07T07:38:49.642842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-10.72477
5-th percentile-7.504767
Q12.8006638
median14.630846
Q327.98181
95-th percentile37.899108
Maximum73.169588
Range83.894358
Interquartile range (IQR)25.181146

Descriptive statistics

Standard deviation14.831216
Coefficient of variation (CV)0.97167276
Kurtosis-0.86665225
Mean15.263591
Median Absolute Deviation (MAD)12.648445
Skewness0.14399843
Sum20147.941
Variance219.96497
MonotonicityNot monotonic
2024-10-07T07:38:49.795064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.94795306 1
 
0.1%
9.262327034 1
 
0.1%
2.844132918 1
 
0.1%
3.261647704 1
 
0.1%
1.815080516 1
 
0.1%
2.24905517 1
 
0.1%
-0.495076603 1
 
0.1%
16.95309452 1
 
0.1%
37.03618549 1
 
0.1%
5.291260873 1
 
0.1%
Other values (1310) 1310
99.2%
ValueCountFrequency (%)
-10.72476975 1
0.1%
-10.65967953 1
0.1%
-9.970262098 1
0.1%
-9.831678296 1
0.1%
-9.825102182 1
0.1%
-9.824642409 1
0.1%
-9.824574766 1
0.1%
-9.733171954 1
0.1%
-9.679928907 1
0.1%
-9.603661067 1
0.1%
ValueCountFrequency (%)
73.16958797 1
0.1%
69.48593769 1
0.1%
59.93197387 1
0.1%
58.10524584 1
0.1%
50.54394309 1
0.1%
47.31817615 1
0.1%
46.80134173 1
0.1%
45.75334767 1
0.1%
42.73966105 1
0.1%
41.89130689 1
0.1%

Vehicle Age (years)
Real number (ℝ)

ZEROS 

Distinct114
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.612843
Minimum0
Maximum11.688592
Zeros143
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-10-07T07:38:49.910503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile7
Maximum11.688592
Range11.688592
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3098236
Coefficient of variation (CV)0.63933683
Kurtosis-1.0448239
Mean3.612843
Median Absolute Deviation (MAD)2
Skewness0.027449843
Sum4768.9528
Variance5.3352851
MonotonicityNot monotonic
2024-10-07T07:38:50.049890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 165
12.5%
6 161
12.2%
5 161
12.2%
3 154
11.7%
4 147
11.1%
0 143
10.8%
1 142
10.8%
2 141
10.7%
0.3556527874 1
 
0.1%
3.73405531 1
 
0.1%
Other values (104) 104
7.9%
ValueCountFrequency (%)
0 143
10.8%
0.020155741 1
 
0.1%
0.08859825775 1
 
0.1%
0.1900469257 1
 
0.1%
0.2884081107 1
 
0.1%
0.3556527874 1
 
0.1%
0.3612151658 1
 
0.1%
0.4616615532 1
 
0.1%
0.5240950281 1
 
0.1%
0.6749470228 1
 
0.1%
ValueCountFrequency (%)
11.68859247 1
 
0.1%
10.63457441 1
 
0.1%
10.54723654 1
 
0.1%
9.076121184 1
 
0.1%
8.018665733 1
 
0.1%
7.690884188 1
 
0.1%
7.545586233 1
 
0.1%
7.51530869 1
 
0.1%
7.197041544 1
 
0.1%
7 165
12.5%

Charger Type
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Level 1
459 
Level 2
431 
DC Fast Charger
430 

Length

Max length15
Median length7
Mean length9.6060606
Min length7

Characters and Unicode

Total characters12680
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDC Fast Charger
2nd rowLevel 1
3rd rowLevel 2
4th rowLevel 1
5th rowLevel 1

Common Values

ValueCountFrequency (%)
Level 1 459
34.8%
Level 2 431
32.7%
DC Fast Charger 430
32.6%

Length

2024-10-07T07:38:50.208496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T07:38:50.306654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
level 890
29.0%
1 459
15.0%
2 431
14.0%
dc 430
14.0%
fast 430
14.0%
charger 430
14.0%

Most occurring characters

ValueCountFrequency (%)
e 2210
17.4%
1750
13.8%
L 890
 
7.0%
v 890
 
7.0%
l 890
 
7.0%
C 860
 
6.8%
a 860
 
6.8%
r 860
 
6.8%
1 459
 
3.6%
2 431
 
3.4%
Other values (6) 2580
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2210
17.4%
1750
13.8%
L 890
 
7.0%
v 890
 
7.0%
l 890
 
7.0%
C 860
 
6.8%
a 860
 
6.8%
r 860
 
6.8%
1 459
 
3.6%
2 431
 
3.4%
Other values (6) 2580
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2210
17.4%
1750
13.8%
L 890
 
7.0%
v 890
 
7.0%
l 890
 
7.0%
C 860
 
6.8%
a 860
 
6.8%
r 860
 
6.8%
1 459
 
3.6%
2 431
 
3.4%
Other values (6) 2580
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2210
17.4%
1750
13.8%
L 890
 
7.0%
v 890
 
7.0%
l 890
 
7.0%
C 860
 
6.8%
a 860
 
6.8%
r 860
 
6.8%
1 459
 
3.6%
2 431
 
3.4%
Other values (6) 2580
20.3%

User Type
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Commuter
476 
Long-Distance Traveler
437 
Casual Driver
407 

Length

Max length22
Median length13
Mean length14.176515
Min length8

Characters and Unicode

Total characters18713
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCommuter
2nd rowCasual Driver
3rd rowCommuter
4th rowLong-Distance Traveler
5th rowLong-Distance Traveler

Common Values

ValueCountFrequency (%)
Commuter 476
36.1%
Long-Distance Traveler 437
33.1%
Casual Driver 407
30.8%

Length

2024-10-07T07:38:50.449446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T07:38:50.556941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
commuter 476
22.0%
long-distance 437
20.2%
traveler 437
20.2%
casual 407
18.8%
driver 407
18.8%

Most occurring characters

ValueCountFrequency (%)
e 2194
 
11.7%
r 2164
 
11.6%
a 1688
 
9.0%
m 952
 
5.1%
t 913
 
4.9%
o 913
 
4.9%
C 883
 
4.7%
u 883
 
4.7%
n 874
 
4.7%
s 844
 
4.5%
Other values (10) 6405
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2194
 
11.7%
r 2164
 
11.6%
a 1688
 
9.0%
m 952
 
5.1%
t 913
 
4.9%
o 913
 
4.9%
C 883
 
4.7%
u 883
 
4.7%
n 874
 
4.7%
s 844
 
4.5%
Other values (10) 6405
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2194
 
11.7%
r 2164
 
11.6%
a 1688
 
9.0%
m 952
 
5.1%
t 913
 
4.9%
o 913
 
4.9%
C 883
 
4.7%
u 883
 
4.7%
n 874
 
4.7%
s 844
 
4.5%
Other values (10) 6405
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2194
 
11.7%
r 2164
 
11.6%
a 1688
 
9.0%
m 952
 
5.1%
t 913
 
4.9%
o 913
 
4.9%
C 883
 
4.7%
u 883
 
4.7%
n 874
 
4.7%
s 844
 
4.5%
Other values (10) 6405
34.2%

Interactions

2024-10-07T07:38:39.661140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:28.768022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.938774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.088354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.196808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.418361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.508549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.634350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.856308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.210890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:39.800976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:28.991149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.053909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.197796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.318471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.644138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.613735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.765864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.988158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.471247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:39.950378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.097298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.156933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.294330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.426898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.736865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.715539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.880646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.117197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.588521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.111662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.217661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.286119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.398743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.556287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.850371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.837307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.011176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.251864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.722631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.224740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.327237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.396820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.522219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.656621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.950145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.926221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.127530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.372554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.843398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.363798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.440744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.504176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.628787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.776263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.035275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.040042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.250145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.514338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.981413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.507467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.544167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.623241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.732406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.888405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.135481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.142473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.380877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.653703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:39.122972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.621841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.644656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.745804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.834709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.016213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.227518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.243211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.514144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.801947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:39.274202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.738813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.755044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.861993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:31.942420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.188281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.325985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.391551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.644389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:37.937526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:39.411117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:40.834401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:29.842721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:30.971290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:32.068440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:33.317020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:34.424912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:35.496231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:36.742972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:38.073982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T07:38:39.514568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-10-07T07:38:41.019318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-07T07:38:41.356497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-07T07:38:41.614779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

User IDVehicle ModelBattery Capacity (kWh)Charging Station IDCharging Station LocationCharging Start TimeCharging End TimeEnergy Consumed (kWh)Charging Duration (hours)Charging Rate (kW)Charging Cost (USD)Time of DayDay of WeekState of Charge (Start %)State of Charge (End %)Distance Driven (since last charge) (km)Temperature (°C)Vehicle Age (years)Charger TypeUser Type
0User_1BMW i3108.463007Station_391Houston2024-01-01 00:00:002024-01-01 00:39:0060.7123460.59136336.38918113.087717EveningTuesday29.37157686.119962293.60211127.9479532.000000DC Fast ChargerCommuter
1User_2Hyundai Kona100.000000Station_428San Francisco2024-01-01 01:00:002024-01-01 03:01:0012.3392753.13365230.67773521.128448MorningMonday10.11577884.664344112.11280414.3110263.000000Level 1Casual Driver
2User_3Chevy Bolt75.000000Station_181San Francisco2024-01-01 02:00:002024-01-01 04:48:0019.1288762.45265327.51359335.667270MorningThursday6.85460469.91761571.79925321.0020022.000000Level 2Commuter
3User_4Hyundai Kona50.000000Station_327Houston2024-01-01 03:00:002024-01-01 06:42:0079.4578241.26643132.88287013.036239EveningSaturday83.12000399.624328199.57778538.3163131.000000Level 1Long-Distance Traveler
4User_5Hyundai Kona50.000000Station_108Los Angeles2024-01-01 04:00:002024-01-01 05:46:0019.6291042.01976510.21571210.161471MorningSaturday54.25895063.743786203.661847-7.8341991.000000Level 1Long-Distance Traveler
5User_6Nissan Leaf50.000000Station_335San Francisco2024-01-01 05:00:002024-01-01 07:10:0043.1811371.16764014.33452336.900341EveningSaturday75.21774871.982288143.680046-5.2742180.000000DC Fast ChargerLong-Distance Traveler
6User_7Chevy Bolt85.000000Station_162Houston2024-01-01 06:00:002024-01-01 07:53:0036.8621403.53961926.18518822.214225EveningFriday60.75178170.79609781.33800927.5513354.000000Level 2Commuter
7User_8Chevy Bolt75.000000Station_302Los Angeles2024-01-01 07:00:002024-01-01 10:42:0051.4676172.65539626.7029089.796821AfternoonMonday56.20170363.786815116.543166-4.4174600.000000Level 2Long-Distance Traveler
8User_9Chevy Bolt62.000000Station_493Los Angeles2024-01-01 08:00:002024-01-01 09:21:0043.5923721.72420414.29492332.465005EveningWednesday33.46620092.961421208.25974222.5167064.000000Level 1Commuter
9User_10Hyundai Kona50.000000Station_452Chicago2024-01-01 09:00:002024-01-01 12:44:0078.8686072.02687511.76100021.312302MorningWednesday27.39945570.05338154.00630927.5120192.830381DC Fast ChargerCommuter
User IDVehicle ModelBattery Capacity (kWh)Charging Station IDCharging Station LocationCharging Start TimeCharging End TimeEnergy Consumed (kWh)Charging Duration (hours)Charging Rate (kW)Charging Cost (USD)Time of DayDay of WeekState of Charge (Start %)State of Charge (End %)Distance Driven (since last charge) (km)Temperature (°C)Vehicle Age (years)Charger TypeUser Type
1310User_1311Tesla Model 362.000000Station_268New York2024-02-24 14:00:002024-02-24 17:50:0066.9799080.77566313.78715138.833010AfternoonSaturday64.12607394.364933NaN36.4320881.0Level 2Casual Driver
1311User_1312Hyundai Kona50.000000Station_13Houston2024-02-24 15:00:002024-02-24 18:53:0074.2767012.84915125.07354438.194006NightTuesday41.34406250.412341194.7935447.7319673.0Level 2Commuter
1312User_1313Nissan Leaf75.000000Station_120Houston2024-02-24 16:00:002024-02-24 18:54:0039.7338531.24929621.39560619.413665MorningTuesday55.86361585.122262NaN24.1089530.0Level 1Casual Driver
1313User_1314Tesla Model 3129.350616Station_458Chicago2024-02-24 17:00:002024-02-24 19:35:0060.7251442.36599110.49192111.289733NightSaturday41.85682594.097883279.552278-1.3699943.0Level 2Long-Distance Traveler
1314User_1315Hyundai Kona50.000000Station_353New York2024-02-24 18:00:002024-02-24 19:58:0043.2514532.50180963.21611836.356930MorningMonday62.10867265.198895220.2818631.6308505.0Level 1Long-Distance Traveler
1315User_1316Nissan Leaf100.000000Station_57New York2024-02-24 19:00:002024-02-24 20:30:0042.0116541.4264445.89547522.081164EveningSunday39.20410283.915952239.6010751.9196557.0DC Fast ChargerCommuter
1316User_1317BMW i3100.000000Station_40New York2024-02-24 20:00:002024-02-24 20:44:0068.1858533.23821218.3880125.067806EveningTuesday31.45637593.096461164.37602234.0297754.0Level 2Casual Driver
1317User_1318Nissan Leaf100.000000Station_374New York2024-02-24 21:00:002024-02-24 23:03:0018.8951023.26712245.48206637.255002EveningTuesday71.90308178.678879226.51925820.3587615.0DC Fast ChargerCommuter
1318User_1319Chevy Bolt85.000000Station_336San Francisco2024-02-24 22:00:002024-02-24 23:20:0013.7562522.75452738.14818339.046146AfternoonSunday76.18799765.926573291.49407624.1345985.0Level 2Commuter
1319User_1320Nissan Leaf120.447195Station_128Los Angeles2024-02-24 23:00:002024-02-24 23:56:0063.6525703.74097033.70422610.863674EveningMonday59.33807656.69243914.449236-6.9665935.0DC Fast ChargerCommuter